Participants’ Perceptions of Advantages and Drawbacks of “Drop-In” Versus “Closed-Group” Formats Related to Cancer Bereavement Program Delivery
Bibliographic record
Abstract
Having opportunities to readily access bereavement support for people affected by the death of a loved one is central to any comprehensive approach to cancer care. Hope & Cope, a community-based cancer support organization in Montreal, Quebec, Canada, offers professional- and volunteer-led bereavement programs in two formats: “drop-in” (open as needed) and “closed-group” (structured). This qualitative study explored contributions and potential drawbacks of these two-program delivery formats as reported by bereaved participants (N = 18). Semi-structured individual interviews were conducted according to groups: Drop-in (n = 7) and closed-group (n = 11). Audio-recorded interviews (lasting between 30 and 60 min) were transcribed verbatim. Data were analyzed using thematic analysis. Three themes were revealed: (1) Program structure according to grief timeline, (2) Flexibility in the choice of topics and impact on grief experiences, (3) Grief support dynamics in relation to group composition. Findings indicate that drop-in provided “as-needed” tailored support, whereas closed-groups ensured consistency in attendance. Some drawbacks included high attendance turnover in the drop-in and less relevant topics in the structured closed format. Supportive interventions should continue to be tailored to people’s profiles and preferences, not only for content but also for delivery formats.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".